Identification of a Notch transcriptomic signature for breast cancer [dataset2]

Dysregulated Notch signalling contributes to breast cancer development and progression, but validated tools to measure the level of Notch signalling in breast cancer subtypes and in response to systemic therapy are largely lacking. A transcriptomic signature of Notch signalling would thus be warranted, and in this report, we have established such a classifier. To generate the signature, we first identified Notch-regulated genes from six basal-like breast cancer cell lines subjected to elevated and reduced Notch signalling by culturing on immobilized Notch ligand Jagged1 or blockade of Notch by ?-secretase inhibitors, respectively. From this cadre of Notch-regulated genes, we developed candidate transcriptomic signatures that were trained on a breast cancer patient dataset (the TCGA-BRCA cohort) and an optimal 20-gene signature was selected from analysis of the transcriptomes of a broader breast cancer cell line cohort. We validated the signature on two independent patient datasets (METABRIC and Oslo2) and it showed an improved coherence score and tumour specificity compared with previously published signatures. Furthermore, the signature score was particularly high for basal-like breast cancer, indicating an enhanced level of Notch signalling in this subtype. The signature score was increased after neoadjuvant treatment in the PROMIX and BEAUTY patient cohorts, and a lower signature score generally correlated with better clinical outcome. Collectively, the 20-gene transcriptional signature has the potential to better stratify patients and to evaluate the response of future Notch-based therapies for breast cancer. Overall design: To generate the signature, we first identified Notch-regulated genes from six basal-like breast cancer cell lines subjected to elevated and reduced Notch signalling by culturing on immobilized Notch ligand Jagged1 or blockade of Notch by g-secretase inhibitors, respectively. From this cadre of Notch-regulated genes, we developed candidate transcriptomic signatures that were trained on a breast cancer patient dataset (the TCGA-BRCA cohort) and a broader breast cancer cell line cohort. RNA libraries of samples used for Notch signature gene determination were constructed as described above and samples were sequenced on NovaSeq6000 (NovaSeq Control Software 1.7.5/RTA v3.4.4) with a 151nt(Read1)-10nt(Index1)-10nt(Index2)-151nt(Read2) setup using 'NovaSeqXp' workflow in 'S4' mode flowcell. The Bcl to FastQ conversion was performed using bcl2fastq_v2.20.0.422 from the CASAVA software suite. The quality scale used is Sanger / phred33 / Illumina 1.8+. Three biological replicates were sequenced per condition. Samples were sequenced on NovaSeq6000 (NovaSeq Control Software 1.7.5/RTA v3.4.4) with a 151nt(Read1)-10nt(Index1)-10nt(Index2)-151nt(Read2) setup using 'NovaSeqXp' workflow in 'S4' mode flowcell. The Bcl to FastQ conversion was performed using bcl2fastq_v2.20.0.422 from the CASAVA software suite. The quality scale used is Sanger / phred33 / Illumina 1.8+. Validated under ISO accreditation 17025 The authors would like to acknowledge support from Science for Life Laboratory, the National Genomics Infrastructure, NGI, and Uppmax for providing assistance in massive parallel sequencing and computational infrastructure.